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International Conference on Data Mining , will be organized around the theme “”

Data Mining Conf 2020 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Data Mining Conf 2020

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With an increase in demand and growth in opportunities in fields of science, technology, engineering and math, women remain underrepresented in these fields. Women are good at communication, and promote a positive atmosphere among the team and are much dedicated in problem solving and decision making. But according to a report given by National Center for Women and Information Technology, number of women in computing occupations has been declining steadily, but the growing awareness is slowly bringing more women into the field. As per a report, women's involvement in information technology patenting has significantly increased from 1.7% in 1980 to 7.8% in 2010. In 2014, 25% of chief data officers were women, whose responsibility is to manage company's data by developing a data governance structure and leverage the data. One in four data scientists are women and they hold only 26% of all data professional positions as per a survey

  • Track 1-1Global women in data science
  • Track 1-2Gender diversity outlook
  • Track 1-3Why we need women in data science?
  • Track 1-4Resources for women in data science

Over past few years, big data has become a game changer in almost all industries. As big data is spreading wide in our daily activities, people are focusing mostly on the value of big data more than its significance. Most of the organizations set up goals for adopting big data projects, which includes goals such as enhancing customer experience, cost reduction, better marketing, effective decision making etc.,  Due to data breaches, which means release of confidential or private data either intentionally or unintentionally, have made security an important goal in big data project to function securely.  Big data enables encompassing the larger picture of all the collected data which further makes it easy to identify patterns and get the required information for problem solving and decision making. In a manufacturing industry, this big data is combined with manufacturing software for data mining

  • Track 2-1Business transformation through big data
  • Track 2-2Industrial big data
  • Track 2-3Industrial growth and development
  • Track 2-4Big data applications in industries
  • Track 2-5Global big data industry
  • Track 2-6Big data and analytics

Deep Learning is technique of machine learning which is concerned with methods based on artificial neural networks and representation learning with algorithms inspired by structure and function of brain, towards its goal of Artificial Intelligence. It uses multiple layers to extract higher level features progressively from the raw input. Architectures of deep learning include deep neural networks, deep belief networks etc., in fields of computer vision, speech recognition, natural language processing, social network filtering, machine translation, material inspection etc. Unsupervised side of the tracks produces more benefits as the field matures and deals with abundance of unlabeled data available. Deep learning models are trained by using a large set of labeled data, substantial computing power and neural networks, achieving accuracy, sometimes exceeding human intelligence. Some of the examples of deep learning include automated driving, electronics, industrial automation etc

  • Track 3-1Artificial neural networks
  • Track 3-2Deep learning vs machine learning
  • Track 3-3Deep learning platforms
  • Track 3-4Deep learning model training
  • Track 3-5Deep learning in cyber security

A set of mathematical instructions that allows a mechanical computer to execute a step by step procedure is called an algorithm. Artificial Intelligence algorithms have the ability to learn from the data and develop new algorithms by learning new heuristics or strategies. These algorithms take both input and output to produce an output when new inputs are given, by which we make our machines to learn the data. Data mining requires this machine learning to recognize the patterns in the data and to get required result. Artificial Intelligence algorithms enable a data scientist to solve different problems such as Classification, Regression and Clustering, where data is the key driver to pick right algorithm to predict outputs from inputs. These are categorized into Supervised learning, Unsupervised learning, Reinforcement learning and Ensemble learning

  • Track 4-1Regression algorithms
  • Track 4-2Clustering algorithms
  • Track 4-3Ensemble learning algorithms
  • Track 4-4Decision trees
  • Track 4-5Naive bayes classification
  • Track 4-6Component analysis
  • Track 4-7Singular value decomposition

Performing computations with the use of quantum mechanical phenomena is called as Quantum Computing. Calculations are performed based on the probability of an object’s state before its measurement in just 1 or 0 seconds by the help of quantum computers, that means it can process more data in less time when compared to that of classical computers. Quantum state of objects called as Qubits, are used in operations by quantum computing using superconductivity for creation and maintenance of a quantum state. These Qubits can be in a superposition, which means they can be both on and off at the same time or in spectrum between the two, thus allowing the probability for uncertainty. Qubits can also perform Entanglement where a pair of particles is generated sharing a spatial proximity, it explains the disparity between classical and quantum physics.

  • Track 5-1Quantum supremacy
  • Track 5-2Models of quantum computing
  • Track 5-3Quantum simulation
  • Track 5-4Quantum operations
  • Track 5-5Cryptography
  • Track 5-6Quantum annealing and adiabatic optimization

Machine Learning platform is a platform for automation and acceleration of the delivery lifecycle of predictive applications which are capable of processing big data using machine learning or related techniques. Many areas like Customer support, Fraud detection, Business Intelligence are making a way for applications in Artificial Intelligence and Machine Learning and are seeking cloud computing platforms for their services. Right from providing new services to making major reorganizations, these two are related in organizational structures of organizations such as Amazon, Google and Microsoft

  • Track 6-1Machine learning algorithms
  • Track 6-2Machine learning workbench and services
  • Track 6-3Analytics and security for machine learning
  • Track 6-4Cloud platforms for machine learning
  • Track 6-5Benefits of machine learning in the cloud
  • Track 6-6Machine learning as a service

Artificial Intelligence is showing its rise all over and it didn’t leave operational activities in business behind. Usage of machine learning, big data analytics, and other artificial technologies for automation of identifying and resolving the common information technology issues, is referred as Artificial Intelligence for IT Operations. Type of intelligence system designed for the sake of real world applications at commercial scale is called as Operational Artificial Intelligence. It is different from fundamental artificial research and industrial artificial intelligence applications which are different from the regular usage of business. However with the introduction of artificial intelligence in business, changes in the job pattern may occur with deletion of some existing jobs and addition of new roles and responsibilities. As IT systems are growing more complex, it is leading to increase subtle faults in IT operations, which are now to be operated by Artificial Intelligence

  • Track 7-1Automation of routine operations
  • Track 7-2Recognizing issues
  • Track 7-3Streamlining the interactions
  • Track 7-4AIOps platforms
  • Track 7-5Breadth of AI capabilities for new IT operations

Creating or producing something to meet the personal requirements of an individual is called as Personalization. Meeting those personal preferences and requirements in machines is possible through deep learning in machine learning. Personalization enables a customer to experience the services which are specifically personalized as per his/her preferences and relevance. This is possible through data learning through the inputs that are provided by the customers and displaying the results as per the customer’s choice. Raw data given by the customers is taken by the machines through multiple processing layers where output of previous layer is taken as input of present layer and so on using deep learning algorithms. This personalization improves the overall experience of the user which helps in increasing sales and better customer engagement in business. Machine learning can enhance personalization through predictive analysis of data. Understanding the data, Content optimization and relevance are the main aspects of personalization with deep learning. 

  • Track 8-1Personalized marketing and marketing automation
  • Track 8-2Using big data to know your customers
  • Track 8-3AI and machine learning in marketing
  • Track 8-4Enhancing personalization
  • Track 8-5Content optimization
  • Track 8-6Better user experience

Process of dividing audience into groups based on their preferences, traits or behavior is called as Segmentation of audience. Machine learning analyses the user base to identify complex patterns and correlations which helps in better personalization. Through this segmentation, marketers would be able to advertise their products or services to those targeted audience which might have a larger chance of acceptance either by sending messages or displaying the product. Because of this, it becomes easy for the marketers to specifically focus on a group of audience and target them for their sales activities. Marketers have to set prioritized goals, test, optimize and revisit their segments for proper analysis of customer segmentation. Activation of new users, Engagement of existing users, building user habits are concerned with proper marketing by targeting segmented audience. 

  • Track 9-1Data analysis techniques for fraud detection
  • Track 9-2Demographic Segmentation
  • Track 9-3Geographic Segmentation
  • Track 9-4Techno graphic Segmentation
  • Track 9-5Psychographic Segmentation
  • Track 9-6Behavioral Segmentation

Deceiving and scamming people in their financial transactions by committing crime is referred to ad Fraud and identifying this fraud with help of some techniques is called as Fraud Detection. Machine learning approach to fraud detection gained much attention in recent times by signaling possible fraud by detecting subtle and hidden events in user behavior. It includes less manual work and faster data processing. Uncontrolled loss of value of something is called as risk and a number which determines the severity of a risk is called as a risk score. Risk scoring can be done by Qualitative and quantitative methodologies, which includes assessment and techniques. Credit risk modelling in machine learning uses financial data to predict the default risk. Artificial neural networks, random forest and boosting are some of the approaches of machine learning related to risk scoring through credit risk modelling

  • Track 10-1Fraud scenarios and detection
  • Track 10-2Fraud detection systems
  • Track 10-3Risk scoring systems
  • Track 10-4Data analysis techniques for fraud detection
  • Track 10-5Calculating risk scores for project risk analysis

A technique that is used to predict the future failure point of a machine using condition monitoring tools that tracks the performance that detects the possible defects to replace the component, as per a plan before the occurrence of failure. Prevention of failure can be done by regularly scheduled and correlative maintenance. It ensures maximum availability of critical manufacturing systems, and minimizing the cost of maintenance and repairs. With artificial intelligence implementation and machine learning projects data collection over time to monitor the state of equipment contributes to predictive maintenance.  By using machine learning in predictive maintenance, we can gather the data, to frame a problem and take strategic decisions

  • Track 11-1Predictive models of machine learning
  • Track 11-2Supervisory control and data acquisition
  • Track 11-3Solving a predictive maintenance business problem through applied data science
  • Track 11-4Predictive maintenance strategies
  • Track 11-5Real-time fault detection‎

A system of inter related computing devices, digital and mechanical machines which are provided with unique identifiers (UID) which is able to transfer the data without any human interaction in the process is called as Internet Of Things (IoT). Organizations are using this IoT for efficient operations, better understanding of customers, proper decision making and to increase the value of business. Automating, optimizing and finding a value by using data science and advanced computer algorithms is done by data analytics and artificial intelligence which further creates transformative business insights. AI algorithms uses data analytics mainly for the purpose of customer interaction through chatbots, e commerce platforms and others.  Data analytics have an impact on business when it is merged with IoT applications and investments in terms of volume, structure, driving revenue, competitive edge and other analytics.

  • Track 12-1Data analytics & artificial intelligence trends
  • Track 12-2Difference between data analytics and AI machine learning
  • Track 12-3The benefits of AI in data analytics
  • Track 12-4Emerging use cases for IoT data analytics
  • Track 12-5Internet of things and big data
  • Track 12-6Internet of things (IoT) analytics

Strategies and techniques which are used to transform the raw data into actionable insights of meaningful information that helps a business to take strategical and tactical decisions of organization through collection, integration, analysis and presentation of business data is called as business intelligence. It is a combination of data mining, business analytics, data visualization and data tools and infrastructure. Creation of key performance indicators, identifying benchmarks, market trends and business problems are some of the uses of business intelligence. Professional data users, information technology users, and business people are the users of this technique. It helps to boost productivity, streamlining business process, easy analytics, accountability and to improve visibility.

  • Track 13-1Business intelligence vs Business analytics
  • Track 13-2Strategies of business intelligence
  • Track 13-3Future of business intelligence
  • Track 13-4Business intelligence software
  • Track 13-5Keys to effective business intelligence

A set of methodologies which are used to analyze data from different angles and perspectives to find hidden patterns for the classification and grouping of data and summarizing the identified relationships and patterns are called as data mining tools. Quick analysis of data can be done by using data mining techniques with proper knowledge on data mining tools to work with. Some of the famous data mining tools include rapidminer, Oracle, Apache, weka, Orange, R, Knime, Rattle and so on. These techniques make use of specific algorithms, statistical analysis, artificial intelligence and database systems to get information from huge datasets.

  • Track 14-1Trending data mining tools
  • Track 14-2Applications of data mining tools
  • Track 14-3Data mining tools and techniques
  • Track 14-4Design and implementation of data mining tools

An artificial intelligence software which is used to simulate a conversation with the user through messaging and mobile applications, websites or telephone is called as a chatbot. It allows the interaction between humans and machines by a question answering system leveraging Natural Language Processing (NLP) formulating responses for end use applications. Main two tasks of chatbot are analysis of user request and return the response to user. Rule based chatbots are built using a graphical user interface while Artificial Intelligence chatbots work based on training by a bot developer. Virtual assistants have much sophisticated interactive platform when compared to that of chatbots in terms of intelligence, natural language processing, tasks and technology. Virtual assistants have a wider scope of performance and uses artificial neural networks for analysis

  • Track 15-1Virtual assistant devices
  • Track 15-2Future of virtual assistants and chatbots
  • Track 15-3Virtual assistant vs chatbots
  • Track 15-4Applications of chatbots
  • Track 15-5Are chatbots the future of marketing?

The ability to embed artificial intelligence methodology with a combination of human capacities to learn, percept and interact at a complex level into core of organization’s data strategy. Semantic search and natural language processing, scaling data governance through automated organization, augmented categorization and data classification, discovering relationships through recommender systems and advanced analytics are some of the applications of enterprise artificial intelligence. Process of integration of digital technologies to create or modify the business process, culture, information and customer experience in all business areas to change the business and market requirements for effective operation and value delivery to the customers is called as Digital transformation. Big data is bringing the ability to transform industries and the potential to turn business models through analysis of patterns in the data allowing organizations to build models that create forecasts under different scenarios.

  • Track 16-1Enterprise AI as an organizational asset
  • Track 16-2Servers and applications of enterprise AI
  • Track 16-3Strategies of enterprise AI
  • Track 16-4Data framework amplifies digital transformation
  • Track 16-5Digital transformation with data science

Artificial intelligence has been influencing various industries by improving the way businesses are being carried out. Understanding how artificial intelligence is influencing the customers and considering all the available data helps business and market to improve in their field. Artificial intelligence shows an impact on consumer behavior like buying behavior and selection, acquiring new customers, applications of virtual assistants, convenience level and trust of the customer. Consumer perceptions, aspirations, emotions, experiences and fears can be improved by artificial intelligence and human interaction. Using data to provide more personalized and targeted products, services, and content is called as Hyper personalization, which is said to be future of marketing as it is opted by many known brands in present world to provide customers the best user experience.

  • Track 17-1Hyper personalization
  • Track 17-2Innovative consumer services by AI
  • Track 17-3Effect of AI on consumer goods industry
  • Track 17-4Retail AI influence on consumer behavior
  • Track 17-5Customer relationship management
  • Track 17-6Future of consumer AI

Artificial Intelligence which is otherwise called as machine intelligence, helps in development of computers and robots which are capable of resolving data to provide requested information, supply analysis, or trigger events based on findings. AI influences business in terms of financial behavior, cybersecurity and human machine interface. Machine Learning, Deep Learning, Natural Language Processing, Neural networks, Computer Vision, Robotics are some of the technologies related to artificial intelligence. Applying biological mechanisms such as thinking, learning and decision capacities of human brain to machines or computers enables them to think like human intelligence with much more accuracy, advancement and speed. Hence developing these artificial intelligence technologies benefits the companies and business in development and technological advancement providing better services to customers.

  • Track 18-1AI optimized hardware
  • Track 18-2Marketing automation
  • Track 18-3Robotic process automation
  • Track 18-4Latest advancements in AI technologies
  • Track 18-5Transformation of world through AI technologies
  • Track 18-6Artificial Intelligence and the future of humans

Robotics use data mining to learn the algorithms for Linked multicomponent robotic systems, Single robot hose transport and Reinforcement learning. Robots works as per data mining by control loops and reactionary mechanisms with the help of machine learning. Inferences from data mining are used in robot control systems for optimizing performance. A program which acts on behalf of organization to perform flexible and autonomous actions that carries complex information and allotted communications tasks using artificial intelligence in pursuit of goal is called as intelligence software agent. They perceive and interpret the data from its environment, reflects events, and take actions to achieve given goals with the help of artificial intelligence

  • Track 19-1Humanoid robots
  • Track 19-2Human robot interaction
  • Track 19-3Robotic sensor data analysis using stream data mining
  • Track 19-4Applications of data mining in robotics
  • Track 19-5Artificial intelligence and robotics
  • Track 19-6Social robotics

Examining large amounts of data to find hidden patterns, correlations and other insights to find out new opportunities such as more efficient operations, higher profits and customer acquisition is called as Big Data Analytics. Through analysis of market’s data, organizations would be able to make faster and better decisions, launching new products and services, overcoming the competition and business development. Big data analytics has its wide range of applications in various industries such as healthcare, banking and securities, manufacturing, electricity, agriculture, education, government sectors, transportation, insurance, energy and utilities, media and entertainment etc

  • Track 20-1Architecture of big data
  • Track 20-2Real time big data applications in various domains
  • Track 20-3Big data processing
  • Track 20-4Tools and techniques for big data
  • Track 20-5Trends in big data analytics